amz-attributes-completer

Original：🇺🇸 English

Translated

Audit and complete the Amazon Seller Central Attributes section for a listing. Diffs the category template against the filled fields, ranks the missing fields by impact on AI-driven search (Rufus, Alexa+) and traditional ranking, and proposes the optimal values. Use when a user asks about the Attributes section, missing attributes, product attributes, category fields, or "how to fill the back end of my listing". Trigger phrases: "attributes", "attributes section", "category fields", "back end fields", "missing attributes". Works with zero tools.

21installs

Sourcejaygptpro/amazon-pro-skills

Added on2026-06-26

NPX Install

npx skill4agent add jaygptpro/amazon-pro-skills amz-attributes-completer

SKILL.md Content

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Attributes Completer

The Seller Central Attributes section is the most important SEO field most sellers ignore. Amazon's AI ranking (Rufus, Alexa+, COSMO) reads attributes before bullets or A+. Most listings fill under 40 percent of available fields. This skill closes that gap with the right values.

When to use this

A new listing being set up and attributes are mostly empty.
An old listing where attributes were never revisited.
AI search visibility is weak (long-tail queries return competitors).
A category template was updated and new fields appeared.

The framework. The Three-Tier Field Priority

Not all attribute fields weigh equally. Fill in this order.

Tier	Fields	Why
Tier 1. Identity	Material, dimensions, weight, color, size, count	Hard filters. shoppers narrow by these. AI uses them as facts
Tier 2. Use case	Compatibility, audience, age range, use, occasion	Drives long-tail and AI question matching
Tier 3. Compliance and brand	Country of origin, batteries, warnings, brand, manufacturer	Required for some categories. avoids suppression

Within each tier, fill every field the category template offers, even if a value seems obvious from the title. The AI does not read inference. it reads the field.

Step by step

Collect inputs. The category, current Attributes section snapshot (what's filled vs empty), the product, any spec sheet.
Pull the category template. Identify every available field. categories vary, and the template adds fields over time.
Diff filled vs available. Build the list of empty fields.
Classify each empty field into Tier 1, 2, or 3.
Propose the optimal value for each empty field. Be specific. "Material: Stainless Steel 304", not "Metal".
Prioritize. Tier 1 first, then 2, then 3. Within a tier, by AI search impact.
Flag any field that requires evidence (compliance, certifications). these need source documents before filling.
Run the quality check, then deliver.

Output format

## Attributes Audit. [ASIN]

Category: [category]
Filled: [%]   Empty fields: [count]

### Fill order
Tier 1 (Identity)
- [field] . current: [empty/value] . proposed: [value]
...
Tier 2 (Use case)
...
Tier 3 (Compliance and brand)
...

### Evidence required
[fields that need source documents before filling]

### Estimated lift
[expected AI-search lift from filling Tier 1 + 2]

Worked example

A kitchen knife listing in Home and Kitchen. Audit shows 9 of 24 category fields filled. Empty Tier 1: blade material, handle material, exact dimensions, weight. Empty Tier 2: handedness, cut type, intended use, knife type, dishwasher safe. Empty Tier 3: country of origin, warranty type.

Plan: fill all Tier 1 fields with specific values (Blade material: VG-10 Damascus steel, not "stainless"). Tier 2 fields with the exact use case wording (Intended use: home cooking, professional chef). Tier 3 last, with documentation where needed. Lift: long-tail queries like "8-inch chef knife Damascus stainless" now match this listing strongly through the AI ranking layer.

Quality check

Every field is classified into a tier.
Tier 1 is filled first, with specific values not generic placeholders.
Compliance fields are flagged for evidence before filling.
Empty fields are prioritized by AI-search impact, not alphabetical.
The estimated lift is realistic, not vague.

Common mistakes

Generic values. "Material: Metal" instead of "Material: Stainless Steel 304". the AI reads specifics.
Skipping fields that "feel obvious". The AI does not infer from the title. it reads the field directly.
Filling Tier 3 first. Compliance and brand fields are important but lower impact on search than the Tier 1 identity fields.
Set and forget. Categories add fields over time. attributes need a quarterly re-audit.

Built by Jay GPT Pro

Part of Amazon Pro Skills. Production-grade skills for serious Amazon sellers. Free and open. Built by Jay Margaliot.

I share a new AI play for Amazon sellers every week, free, in my WhatsApp group. Join here: https://chat.whatsapp.com/ILX65p1yWcaIG3c9WGHpTY